Computational Toxicology最新文献

{"title":"A computational model of endogenous hydrogen peroxide metabolism in hepatocytes, featuring a critical role for GSH","authors":"L.M. Bilinsky","doi":"10.1016/j.comtox.2024.100299","DOIUrl":"10.1016/j.comtox.2024.100299","url":null,"abstract":"<div><p>This paper presents an ordinary differential equation (ODE) model of endogenous H₂O₂ <!-->metabolism in hepatocytes that is unique, at the time of writing, in its ability to accurately compute intracellular H₂O₂ <!-->concentration during incidents of oxidative stress and in its usefulness for constructing PBPK/PD models for ROS-generating xenobiotics. Versions of the model are presented for rat hepatocytes <em>in vitro</em> and mouse liver <em>in vivo</em>. A generic method is given for using the model to create PBPK/PD models which predict intracellular H₂O₂ <!-->concentration and oxidative-stress-induced hepatocyte death; these are identifiable from <em>in vitro</em> data sets reporting cell mortality following xenobiotic exposure at various levels. The procedure is demonstrated for the trivalent arsenical dimethylarsinous acid (DMA^<em>III</em>), which is produced in liver as part of the arsenic elimination pathway. This is the first model of H₂O₂ <!-->metabolism in hepatocytes to feature values for the endogenous rates of H₂O₂ <!-->production by mitochondria and other organelles which are inferred from the physiology literature, and to feature a detailed, realistic treatment of GSH metabolism; the latter is achieved by incorporating a minimal version of Reed and coworkers’ pioneering model of GSH metabolism in liver. Model simulations indicate that critical GSH depletion is the immediate trigger for intracellular H₂O₂ <!-->rising to concentrations associated with apoptosis (<span><math><mrow><mo>&gt;</mo><mn>1</mn><mi>μ</mi><mi>M</mi></mrow></math></span>), that this may only occur hours after the xenobiotic concentration peaks (“delay effect”), that when critical GSH depletion does occur, H₂O₂ <!-->concentration rises rapidly in a sequence of two boundary layers, characterized by the kinetics of glutathione peroxidase (first boundary layer) and catalase (second boundary layer), and that intracellular H₂O₂ <!-->concentration <span><math><mrow><mo>&gt;</mo><mn>1</mn><mi>μ</mi><mi>M</mi></mrow></math></span> implies critical GSH depletion. There has been speculation that ROS levels in the range associated with apoptosis simply indicate, rather than cause, an apoptotic milieu. Model simulations are consistent with this view. In a result of interest to the wider physiology community, the delay effect is shown to provide a GSH-based mechanism by which cells can distinguish transient elevations in H₂O₂ <!-->concentration, of use in intracellular signaling, from persistent ones indicative of either pathology or the presence of toxins, the second state of affairs eventually triggering apoptosis.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139640107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Confidence score calculation for the carcinogenic potency categorization approach (CPCA) predictions for N-nitrosamines","authors":"Suman Chakravarti,&nbsp;Roustem D. Saiakhov,&nbsp;Mounika Girireddy","doi":"10.1016/j.comtox.2023.100298","DOIUrl":"https://doi.org/10.1016/j.comtox.2023.100298","url":null,"abstract":"<div><p>We present a method for computing confidence in the Carcinogenic Potency Categorization Approach (CPCA) based predictions for N-nitrosamines. Our method relies on capturing local structural variations surrounding the nitrosamine core, which can significantly influence potency and may introduce uncertainty into predictions relying on these features.</p><p>We use continuous-valued fingerprints to conduct a specialized neighborhood analysis, grouping nitrosamines with similar local features. Using a reference dataset of 7679 potential Nitrosamine Drug Substance Related Impurities (NDSRIs) with pre-computed CPCA-derived Acceptable Intake (AI) limits, we gauge the prediction confidence for a given query N-nitrosamine by evaluating the distances and CPCA derived potency category distribution among neighboring NDSRIs. Our methodology allows for a nuanced assessment of CPCA's discrete four-level outcomes (i.e. 18/26.5, 100, 400, and 1500 ng AI limits). It enables the differentiation of robust predictions from potentially uncertain ones, for instance, cases where low confidence arises from rare structural features in the query nitrosamine, helpful in regulatory decision-making.</p><p>In our analysis of 30 nitrosamines with animal carcinogenicity data, we often observed lower confidence scores when experimental TD₅₀ values significantly disagreed with CPCA-calculated potency. Moreover, lower confidence scores were associated with greater variability in the predicted α-carbon hydroxylation potential of neighboring compounds. In a list of 265 NDSRIs with established regulatory AI limits, approximately 68% received strong confidence scores for accurate CPCA potency class predictions. However, 8% received poor confidence in potency class predictions, as well as lacked sufficient neighbor support due to uncommon structural features.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139100547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utilizing integrated testing strategy (ITSv1) defined approach and read across to predict skin sensitization of cannabidiol","authors":"Ramez Labib,&nbsp;Ripal Amin,&nbsp;Chris Bartlett,&nbsp;Lisa Hoffman","doi":"10.1016/j.comtox.2023.100297","DOIUrl":"https://doi.org/10.1016/j.comtox.2023.100297","url":null,"abstract":"<div><p>Cannabidiol (CBD) is increasingly being used as an ingredient in cosmetics, but to date no pre-clinical studies have been published to address the skin sensitization end point. This case study investigated its skin sensitization potential for use in a face cream application at 0.3 % using Next Generation Risk Assessment (NGRA) framework. Based on chemical structure and <em>in-silico</em> prediction using Derek Nexus, CBD was predicted to be weak sensitizer with a resorcinol alert moiety. <em>In vitro</em> testing was conducted confirming it to be sensitizer, but the New Approach Methodologies (NAM) data could not provide sufficient confidence to determine a point of departure (PoD). Integrated testing strategy (ITS)v1 Defined Approach (DA), adopted in OECD Guideline No. 497, was used for skin sensitization potency categorization. However, ITSv1 DA alone is not used for further refinement of the potency prediction based on EC3 (the estimated concentration that produces a stimulation index of 3 in murine local lymph node assay) values. Therefore, the application of read-across using Derek Nexus derived a PoD derived from the LLNA EC3 of 42 %. This led to a favorable NGRA conclusion and to support use of CBD at 0.3 % in face cream application.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139100704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Salivary therapeutic monitoring of methadone toxicity in neonates after transplacental transfer from parturient mothers treated with oral methadone guided by PBPK modeling","authors":"Mo'tasem M. Alsmadi","doi":"10.1016/j.comtox.2023.100296","DOIUrl":"https://doi.org/10.1016/j.comtox.2023.100296","url":null,"abstract":"<div><p>Opioid use disorders (OUD) during pregnancy are related to neonatal opioid withdrawal syndrome (NOWS). R,S-methadone used to treat OUD and NOWS can penetrate the placenta. High neonatal brain extracellular fluid (bECF) levels of R,S-methadone can induce respiratory depression in newborns. The purpose of this work was to estimate neonatal bECF and saliva levels to establish the neonatal R,S-methadone salivary thresholds for respiratory depression after maternal oral dosing despite the sparse data in pregnancy and newborn populations. An adult physiologically-based pharmacokinetic (PBPK) model for R,S-methadone after intravenous and oral administration was constructed, vetted, and scaled to newborn and pregnancy populations. The pregnancy model predicted the R-methadone and S-methadone doses transplacentally transferred to newborns. Then, the newborn PBPK model was used to estimate newborn exposure after such doses. After maternal oral dosing of R,S-methadone (43.8 mg/day), the neonatal plasma levels were below the respiratory depression threshold. Further, the bECF levels were above the analgesia threshold for more than 96 h. The salivary thresholds for the analgesic effects of R-methadone, S-methadone, and R,S-methadone were estimated herein at 1.7, 43, and 16 ng/mL, respectively. Moreover, the salivary thresholds for the respiratory depression of R-methadone and R,S-methadone were estimated at 58 and 173 ng/mL, respectively. Using neonatal salivary monitoring of methadone can be useful in ensuring newborns' safety during maternal OUD treatment.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138656177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An overview of conceptual-DFT based insights into global chemical reactivity of volatile sulfur compounds (VSCs)","authors":"Manjeet Bhatia","doi":"10.1016/j.comtox.2023.100295","DOIUrl":"https://doi.org/10.1016/j.comtox.2023.100295","url":null,"abstract":"<div><p><span>Volatile sulfur compounds (VSCs) are highly volatile and most frequently associated with oral malodor. The odor quality is associated with the size and shape of the molecule along with stability, hydrogen bonding, extended d-shell electronic behavior, and complicity of d-shell bonding. Chemical reactivity descriptors of VSCs, such as chemical hardness (</span><em>η</em>), softness (<em>σ</em>), chemical potential (<em>μ</em><span>), electrophilic index (</span><em>ω</em><span>), and electronegativity (</span><em>χ</em>) are computed at B₃<span>LYP/Aug-cc-PVTZ level of theory from the highest occupied molecular orbital<span> (HOMO) and the lowest unoccupied molecular orbital (LUMO) in the light of Koopmans’ approximation. Furthermore, the global reactivity parameters are evaluated from the vertical ionization potential (IP) and electron affinity (EA) to support the results of Koopmans’ theorem. These reactivity parameters offer a quantitative measure of the electronic structure and chemical properties of VSCs, offering insights into their stability, reactivity, and interaction with other molecules. A Python-based application is provided for the rapid calculation of these parameters (GitHub: Link).</span></span></p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138656148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Read-Across to build Physiologically-Based Kinetic models: Part 2. Case studies for atenolol and flumioxazin","authors":"Courtney V. Thompson ,&nbsp;Steven D. Webb ,&nbsp;Joseph A. Leedale ,&nbsp;Peter E. Penson ,&nbsp;Alicia Paini ,&nbsp;David Ebbrell ,&nbsp;Judith C Madden","doi":"10.1016/j.comtox.2023.100293","DOIUrl":"10.1016/j.comtox.2023.100293","url":null,"abstract":"<div><p>Read-across, wherein information from a data-rich chemical is used to make a prediction for a similar chemical that lacks the relevant data, is increasingly being accepted as an alternative to animal testing. Identifying chemicals that can be considered as similar (analogues) is crucial to the process. Two resources have been developed previously to address the issue of analogue selection and facilitate physiologically-based kinetic (PBK) model development, using read-across. Chemical-specific PBK models, available in the literature, were collated to form a PBK model dataset (PMD) of over 7,500 models. A KNIME workflow was created to accompany this PMD that can aid the selection of appropriate chemical analogues from chemicals within this dataset (i.e. chemicals that are similar to a target of interest and are known to have an existing PBK model). Information from the PBK model for the source chemical can then be used in a read-across approach to inform the development of a new PBK model for the target. The application of these resources is tested here using two case studies (i) for the drug atenolol and (ii) for the plant protection product, flumioxazin. New PBK models were constructed for these two target chemicals using data obtained from source chemicals, identified by the workflow as being similar (analogues). In each case, the published PBK model for the source chemical was initially reproduced, as accurately as possible, before being adapted and used as a template for the target chemical. The performance of the new PBK models was assessed by comparing simulation outputs to existing data on key kinetic properties for the targets. The results demonstrate that a read-across approach can be successfully applied to develop new PBK models for data-poor chemicals, thus enabling their deployment during early-stage risk assessment. This assists prediction of internal exposure whilst reducing reliance on animal testing.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468111323000348/pdfft?md5=6b457a68b48b91543a4c7e296decc964&pid=1-s2.0-S2468111323000348-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138621917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Guided optimization of ToxPi model weights using a Semi-Automated approach","authors":"Jonathon F. Fleming ,&nbsp;John S. House ,&nbsp;Jessie R. Chappel ,&nbsp;Alison A. Motsinger-Reif ,&nbsp;David M. Reif","doi":"10.1016/j.comtox.2023.100294","DOIUrl":"10.1016/j.comtox.2023.100294","url":null,"abstract":"<div><p>The Toxicological Prioritization Index (ToxPi) is a visual analysis and decision support tool for dimension reduction and visualization of high throughput, multi-dimensional feature data. ToxPi was originally developed for assessing the relative toxicity of multiple chemicals or stressors by synthesizing complex toxicological data to provide a single comprehensive view of the potential health effects. It continues to be used for profiling chemicals and has since been applied to other types of “sample” entities, including geospatial (e.g. county-level Covid-19 risk and sites of historical PFAS exposure) and other profiling applications. For any set of features (data collected on a set of sample entities), ToxPi integrates the data into a set of weighted slices that provide a visual profile and a score metric for comparison. This scoring system is highly dependent on user-provided feature weights, yet users often lack knowledge of how to define these feature weights. Common methods for predicting feature weights are generally unusable due to inappropriate statistical assumptions and lack of global distributional expectation. However, users often have an inherent understanding of expected results for a small subset of samples. For example, in chemical toxicity, prior knowledge can often place subsets of chemicals into categories of low, moderate or high toxicity (reference chemicals). Ordinal regression can be used to predict weights based on these response levels that are applicable to the entire feature set, analogous to using positive and negative controls to contextualize an empirical distribution. We propose a semi-supervised method utilizing ordinal regression to predict a set of feature weights that produces the best fit for the known response (“reference”) data and subsequently fine-tunes the weights via a customized genetic algorithm. We conduct a simulation study to show when this method can improve the results of ordinal regression, allowing for accurate feature weight prediction and sample ranking in scenarios with minimal response data. To ground-truth the guided weight optimization, we test this method on published data to build a ToxPi model for comparison against expert-knowledge-driven weight assignments.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138625224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using read-across to build physiologically-based kinetic models: Part 1. Development of a KNIME workflow to assist analogue selection for PBK modelling","authors":"Courtney V. Thompson ,&nbsp;Steven D. Webb ,&nbsp;Joseph A. Leedale ,&nbsp;Peter E. Penson ,&nbsp;Alicia Paini ,&nbsp;David Ebbrell ,&nbsp;Judith C. Madden","doi":"10.1016/j.comtox.2023.100292","DOIUrl":"10.1016/j.comtox.2023.100292","url":null,"abstract":"<div><p>Read-across refers to the process by which information from one (source) chemical is used to infer information about another similar (target) chemical. This method can be used to fill data gaps and so inform safety assessment where data are lacking for chemicals of interest. As one chemical cannot be considered as absolutely similar to another, only similar with respect to a given property, it is essential to justify the selection of similar chemicals (analogues) for the purposes of read-across. A previously created dataset of available physiologically-based kinetic (PBK) models (referred to as the PBK modelling dataset or PMD) was used in the development of a KNIME workflow. KNIME is a freely-available, open-source analytics platform that allows users to create workflows to analyse and visualise data. The KNIME workflow described here was designed to identify chemical analogues with a corresponding model in the PMD. The PMD combined with the KWAAS enables PBK model information from source chemical(s) to be used in a read-across approach to help develop new PBK models for target chemicals. This KNIME workflow was applied to six chemicals, representing different types of chemical classes (drugs, cosmetics, botanicals, industrial chemicals, pesticides, and food additives) to assess its applicability across various industries. Information acquired from these PBK models can be used to support safety assessment of chemicals and reduce reliance on animal testing.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468111323000336/pdfft?md5=9d688403f4e3cdfc0ba25d62d7fa9b35&pid=1-s2.0-S2468111323000336-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138621767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structural characterization of permethrin-human hemoglobin binding using various molecular docking tools","authors":"Shweta Singh,&nbsp;Priyanka Gopi,&nbsp;Prateek Pandya,&nbsp;Jyoti Singh","doi":"10.1016/j.comtox.2023.100291","DOIUrl":"https://doi.org/10.1016/j.comtox.2023.100291","url":null,"abstract":"<div><p>A molecular docking investigation was conducted to study the interaction between permethrin (PMT), a commonly used pyrethroid insecticide, known for its toxic effects on various organisms, including insects, aquatic life, and mammals, including humans with hemoglobin (HB). To assess its potential binding with the HB target, molecular docking simulations were conducted using different software. Each software has unique algorithms and scoring methods. Employing multiple tools helped us confirm and understand the interaction better. The results indicated high binding strengths across the various docking web servers. The PMT-HB complexation was largely stabilized via the hydrophobic interactions and Van der Waals forces. Also, PMT exhibited binding at a significant distance from the heme, indicating that it does not interfere with the essential biological function of HB, which is the binding of oxygen. In addition, the analysis of toxicological parameters revealed that PMT possesses the ability to induce acute oral and dermal toxicity.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138472170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toxicokinetic modeling of the transfer of polychlorinated biphenyls (PCBs) and polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) into milk of high-yielding cows during negative and positive energy balance","authors":"Jan-Louis Moenning ,&nbsp;Julika Lamp ,&nbsp;Karin Knappstein ,&nbsp;Joachim Molkentin ,&nbsp;Andreas Susenbeth ,&nbsp;Karl-Heinz Schwind ,&nbsp;Sven Dänicke ,&nbsp;Peter Fürst ,&nbsp;Hans Schenkel ,&nbsp;Robert Pieper ,&nbsp;Torsten Krause ,&nbsp;Jorge Numata","doi":"10.1016/j.comtox.2023.100290","DOIUrl":"10.1016/j.comtox.2023.100290","url":null,"abstract":"<div><p>A toxicokinetic modeling approach was used to study the transfer of 7 polychlorinated dibenzo-<em>p</em>-dioxins (PCDDs), 10 dibenzofurans (PCDFs), 12 dioxin-like polychlorinated biphenyls (dl-PCB) and 3 non-dioxin like (ndl) PCBs in dairy cows. The model describes the concentration–time profile of each congener in milk and blood of high-yielding dairy cows. It was parametrized using an in-house transfer study with 3 cows exposed to a defined synthetic congener mixture for two dosing periods, as well as 3 control cows to account for background exposure. The first dosing was administered during negative energy balance (NEB) after calving, and the second during positive energy balance (PEB) in late lactation. Results include extrapolated steady-state transfer rates and elimination half-lives, many of which have never been reported before. Transfer rates (<em>TR</em>s) were significantly higher during the NEB by a median of 27%, likely due to an increase in non-milk elimination during PEB. The difference draws attention to the influence of the metabolic state of food-producing animals in risk assessment. Comparison of the <em>TR</em>s derived here with those reported in the literature showed that they were, in median, 43% higher in the NEB phase and 16% higher in the PEB phase probably because we report <em>TR</em>s in steady-state unlike most literature sources.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468111323000312/pdfft?md5=cf199e8430917ae3b8bcae74416bfd03&pid=1-s2.0-S2468111323000312-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135763702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}